Why don't integrated gradients explain samples correctly?
After several tests, I've opted for the following configuration:
Approximation mode: riemann trapezoidal
Steps: 1000
Baseline: average mean of each column (feature)
Although the global score remains good, there're important variations in the number of correctly explained local samples. For example, an execution returns 200 out of 390 samples explained as expected, but the following execution only 17.
Is there a reason for that? And, is there a way to make the algorithm predictable?